Evaluation of clouds in GCMs using ARM-data: A time-step approach

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1 Evaluation of clouds in GCMs using ARM-data: A time-step approach K. Van Weverberg 1, C. Morcrette 1, H.-Y. Ma 2, S. Klein 2, M. Ahlgrimm 3, R. Forbes 3 and J. Petch 1 MACCBET Symposium, Royal Meteorological Institute, Belgium Crown Copyright MetOffice 1 Met Office, Exeter, United Kingdom 2 Lawrence Livermore National Laboratory, Livermore, California 3 European Centre for Medium-Range Weather Forecasting, Reading, United Kingdom

2 Midlatitude Continental Warm Bias 2m-Temperature Bias (K) Ma et al. JC 2014 Colours: Dots: CMIP5 ensemble for 20 years Day 5 forecast using same GCMs

3 Midlatitude Continental Warm Bias Hypotheses: Soil-vegetation-atmosphere Boundary-layer clouds Convective storms

4 Midlatitude Continental Warm Bias Hypotheses: Soil-vegetation-atmosphere Boundary-layer clouds Convective storms

5 Midlatitude Continental Convective Clouds Experiment (MC3E) Based around the ARM SGP Central Facility (Oklahoma) Led by DoE ARM and NASA Additional sounding array Additional radars and profilers 6 weeks IOP: April-June 2011

6 Outline Part I: Using ARM-data to understand the nature and origin of a bias in GCMs (CAUSES-project) Focus on the 2m-temperature bias and its relation with clouds Part II: Using ARM-data to test basic assumptions in a GCM cloud parameterisation What is the observed subgrid-variability and how does this compare to assumptions made in the large-scale cloud scheme?

7 Part I Clouds Above the US and Errors at the Surface (CAUSES)

8 Clouds Above the US and Errors at the Surface (CAUSES) Use ARM data to understand the role of clouds in the creation of the US warm bias 2 GCM simulations: 4-day global hindcasts for MC 3 E-period (6 weeks) in 2011 MetUM: driven by EC-oper Δx = 30 km CAM5: driven by ERA-Interim Δx = 100 km Microbase (obs) MetUM (EC-oper)

9 Clouds Above the US and Errors at the Surface (CAUSES) Use ARM data to understand the role of clouds in the creation of the US warm bias 2 GCM simulations: 4-day global hindcasts for MC 3 E-period (6 weeks) in 2011 MetUM: driven by EC-oper Δx = 30 km CAM5: driven by ERA-Interim Δx = 100 km Transpose-AMIP simulations: run climate model in NWP mode Day 1 Day 2 Day 3 Day 4 Forecast Time

10 Correlation of temperature and cloud errors Use ARM data to understand the role of clouds in the creation of the US warm bias Average evolution of the T-bias over the 4 forecast days

11 Correlation of temperature and cloud errors Use ARM data to understand the role of clouds in the creation of the US warm bias Average evolution of the T-bias over the 4 forecast days No clear growth of bias over 4-day hindcast Distinct diurnal cycle in T-bias in both GCMs

12 Correlation of temperature and cloud errors Use ARM data to understand the role of clouds in the creation of the US warm bias Average evolution of the T-bias over the 4 forecast days Cloud biases seem to correlate with T-bias for MetUM, but not for CAM5

13 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases

14 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases Model-state (absolute) biases contain long memory, superposing previous effects

15 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases Model-state (absolute) biases contain long memory, superposing previous effects Ambiguous since averaging over many different regimes that can exhibit opposite effects Many processes could be working together (clouds, land surface, boundary layer)

16 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases Model-state (absolute) biases contain long memory, superposing previous effects Ambiguous since averaging over many different regimes that can exhibit opposite effects Many processes could be working together (clouds, land surface, boundary layer) So, more natural to look at regime-dependent evaluation of model increments: Δt ΔT bias t n t n+1

17 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases Model-state (absolute) biases contain long memory, superposing previous effects Ambiguous since averaging over many different regimes that can exhibit opposite effects Many processes could be working together (clouds, land surface, boundary layer) So, more natural to look at regime-dependent evaluation of model increments: Model increment ΔT only contains memory of this time step, so the error-growth ΔT bias can be more unambiguously traced to coincident model deficiencies Δt ΔT bias t n t n+1

18 Time-step approach for model evaluation Common practice model evaluation: establish relations between averaged model-state biases Model-state (absolute) biases contain long memory, superposing previous effects Ambiguous since averaging over many different regimes that can exhibit opposite effects Many processes could be working together (clouds, land surface, boundary layer) So, more natural to look at regime-dependent evaluation of model increments: Model increment ΔT only contains memory of this time step, so the error-growth ΔT bias can be more unambiguously traced to coincident model deficiencies Regime-dependent analysis allows separating ΔT bias under different atmospheric conditions Compositing the ΔT bias by coincident biases in cloud properties allows to disentangle cloudrelated error-growth from other processes Δt ΔT bias t n t n+1

19 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by coincident biases in cloud properties Clouds influence surface temperature through downwelling radiation (SW + LW ) :

20 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by coincident biases in cloud properties Clouds influence surface temperature through downwelling radiation (SW + LW ) : GOODRAD: Unbiased downwelling radiation as proxy for well-captured cloud properties BIASRAD: Biased downwelling radiation as proxy for poorly-captured cloud properties GOODRAD BIASRAD

21 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by coincident biases in cloud properties Diurnal cycle of mean ΔT bias MetUM (EC oper)

22 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by coincident biases in cloud properties BIASRAD GOODRAD MetUM (EC oper)

23 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by coincident biases in cloud properties GOODRAD BIASRAD Significant land/bl impact Significant cloud impact MetUM (EC oper)

24 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD BIASRAD 12 cloud regimes based on cloud occurrence at 3 levels Each time step assigned to observed/simulated regime pair Composite ΔT bias by observed/simulated cloud regime pair MetUM (EC oper)

25 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD BIASRAD Upwelling bias Downwelling bias MetUM (EC oper)

26 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD BIASRAD Up Down Lack of deep (convective) cloud / Too transparent cirrus MetUM (EC oper)

27 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD BIASRAD Up Down Too abundant lowlevel cloud MetUM (EC oper)

28 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level MetUM (EC oper): GOODRAD BIASRAD Up Down Morning cooling and evening warming: LS/BL issue Afternoon warming: Missing deep cloud Nocturnal warming: Too abundant lowlevel cloud

29 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD CAM5 (ERA-I): BIASRAD Up Down

30 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD CAM5 (ERA-I): BIASRAD Warming originates from LS/BL Up Down

31 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD CAM5 (ERA-I): BIASRAD Up Down Too low albedo

32 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level GOODRAD CAM5 (ERA-I): BIASRAD Up Down Too persistent deep-convective clouds at night

33 Time-step approach for model evaluation Compositing of ΔT bias (error growth) by cloud-regime at Time-step level CAM5 (ERA-I): GOODRAD BIASRAD Up Down Morning cooling and afternoon warming: LS/BL issue Evening warming: Too persistent deep cloud Morning cooling compensated by too little low-level cloud

34 Time-step approach for model evaluation Impact of driving analysis:

35 Time-step approach for model evaluation Impact of driving analysis: MetUM (EC Oper): MetUM (ERA-I): MetUM (UKMO):

36 Time-step approach for model evaluation Impact of driving analysis: Large impact of LS/BL on morning cooling MetUM (EC Oper): MetUM (ERA-I): MetUM (UKMO):

37 Time-step approach for model evaluation Impact of driving analysis: All have lack of afternoon deep cloud and excess nocturnal low cloud MetUM (EC Oper): MetUM (ERA-I): MetUM (UKMO):

38 Part II Testing parameterisation assumptions using ARM-data

39 Cloud Parameterisations Without parameterised cloud fraction: cloud fraction in a grid box is 0 or 1 q s q t

40 Cloud Parameterisations Without parameterised cloud fraction: cloud fraction in a grid box is 0 or 1 q t s

41 Cloud Parameterisations Cloud parameterisation accounts for subgrid variability by assuming a moisture and temperature PDF Allows to start form cloud before whole grid box is saturated But not straightforward to constrain the shape and width of the moisture PDF q s q t

42 Cloud Parameterisations Cloud parameterisation accounts for subgrid variability by assuming a moisture and temperature PDF ARM observations have sufficient resolution to constrain PDF q s q t

43 Cloud Parameterisations Cloud parameterisation accounts for subgrid variability by assuming a moisture and temperature PDF ARM observations have sufficient resolution to constrain PDF Use 2D time/height slice as proxy for 3D grid box variance q s q t Δt Δx

44 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements

45 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km)

46 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km) Variance after noise filtering following Lenschow et al. (2000) and Turner et al. (2014)

47 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km) Most cloud models revolve around Critical Relative Humidity, linked to variance q s q t RH crit RH

48 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km) Most cloud models revolve around Critical Relative Humidity, linked to variance q s q t RH crit RH

49 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km) Most cloud models revolve around Critical Relative Humidity, linked to variance q s q t RH crit RH

50 Cloud Parameterisations From raw data to useful model diagnostic Original Raman Lidar Measurements Variance assuming GCM grid box size (Δx = 30 km) Critical Relative Humidity when cloud should be initiated in GCM grid box

51 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion

52 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion Distinct diurnal cycle in RH crit

53 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion Distinct diurnal cycle in RH crit RH crit larger than default Default RH crit Updated RH crit

54 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion P C l 1 C l GOES-satellite Cloud Optical Depth

55 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion P C l 1 C l Cloud mask

56 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion P C l 1 C l Obtain total cloud perimeter within GCM grid box

57 Cloud Parameterisations From raw data to useful model diagnostic Modified RH crit Modified Cloud Erosion P C l 1 C l Diurnal cycle in cloud perimeter (via α)

58 Cloud Parameterisations Sensitivity study: Do new assumptions of subgrid variability improve the warm bias in SGP*? During the MC3E period: MetUM (old SGS assumptions) MetUM (new SGS assumptions) * Assumptions include RH crit, shape of the PDF, cloud erosion rate

59 Cloud Parameterisations Sensitivity study: Do new assumptions of subgrid variability improve the warm bias in SGP? During the MC3E period: MetUM (old SGS assumptions) MetUM (new SGS assumptions) Afternoon lack of deep convection slightly improved

60 Cloud Parameterisations Sensitivity study: Do new assumptions of subgrid variability improve the warm bias in SGP? During the MC3E period: MetUM (old SGS assumptions) MetUM (new SGS assumptions) Nocturnal overestimation of low cloud deteriorated

61 Cloud Parameterisations Sensitivity study: Do new assumptions of subgrid variability improve the warm bias in SGP? During the 20 year AMIP climate simulation: MetUM (old SGS assumptions) MetUM (new SGS assumptions) Difference

62 Cloud Parameterisations Sensitivity study: Do new assumptions of subgrid variability improve the warm bias in SGP? During the 20 year AMIP climate simulation: MetUM (old SGS assumptions) MetUM (new SGS assumptions) Difference Significant decrease of surface warm bias

63 Conclusions Part I: Top-down model evaluation Evaluating the time step increments rather than the model s state Two-step methodology: 1) Do clouds play a role? Composite the ΔT bias by coincident downwelling radiation bias 2) Which clouds play a role? Apply regime-dependent analysis to find contribution Results for 3 GCMs: MetUM: Afternoon T bias growth due to too transparent cirrus and lack of deep clouds; Too persistent boundary-layer clouds in evening and at night CAM5: Too persistent deep clouds in evening. Afternoon warming due to surface albedo ECMWF: Too small deep cloud fractions throughout the day/night Morning bias reduction in 3 GCMs not related to clouds Role of analysis: MetUM: Cloud-related biases seem fairly consistent for different (re-)analyses, but morning bias reduction very dependent on the analysis

64 Conclusions Part I: Top-down model evaluation Evaluating the time step increments rather than the model s state Two-step methodology: 1) Do clouds play a role? Composite the ΔT bias by coincident downwelling radiation bias 2) Which clouds play a role? Apply regime-dependent analysis to find contribution Results for 3 GCMs: MetUM: Afternoon T bias growth due to too transparent cirrus and lack of deep clouds; Too persistent boundary-layer clouds in evening and at night CAM5: Too persistent deep clouds in evening. Afternoon warming due to surface albedo ECMWF: Too small deep cloud fractions throughout the day/night Morning bias reduction in 3 GCMs not related to clouds Role of analysis: MetUM: Cloud-related biases seem fairly consistent for different (re-)analyses, but morning bias reduction very dependent on the analysis

65 Conclusions Part I: Top-down model evaluation Evaluating the time step increments rather than the model s state Two-step methodology: 1) Do clouds play a role? Composite the ΔT bias by coincident downwelling radiation bias 2) Which clouds play a role? Apply regime-dependent analysis to find contribution Results for 3 GCMs: MetUM: Afternoon T bias growth due to too transparent cirrus and lack of deep clouds; Too persistent boundary-layer clouds in evening and at night CAM5: Too persistent deep clouds in evening. Afternoon warming due to surface albedo ECMWF: Too small deep cloud fractions throughout the day/night Morning bias reduction in 3 GCMs not related to clouds Role of analysis: MetUM: Cloud-related biases seem fairly consistent for different (re-)analyses, but morning bias reduction very dependent on the analysis

66 Conclusions Part II: Bottom-up model development ARM-observations have sufficient resolution and quality to test assumptions on subgrid variability Critical relative humidity typically larger-than default throughout boundary layer, except near top of mixed-layer (based on Microbase and Lidar) Shape of the PDF not triangular, but Gaussian (based on Microbase and Lidar) Cloud erosion smaller than default, based on satellite Updated version of cloud scheme implemented in NWP and climate mode Daytime lack of deep clouds improved, but persistent nocturnal low-level clouds exacerbated Significant improvement of the overall warm bias in the SGP

67 Conclusions Part II: Bottom-up model development ARM-observations have sufficient resolution and quality to test assumptions on subgrid variability Critical relative humidity typically larger-than default throughout boundary layer, except near top of mixed-layer (based on Microbase and Lidar) Shape of the PDF not triangular, but Gaussian (based on Microbase and Lidar) Cloud erosion smaller than default, based on satellite Updated version of cloud scheme implemented in NWP and climate mode Daytime lack of deep clouds improved, but persistent nocturnal low-level clouds exacerbated Significant improvement of the overall warm bias in the SGP

68 Outlook Part I will be part of the CAUSES project Project with observationally-based focus, which evaluates the role of clouds, radiation and precipitation processes contributing to the surface temperature biases in the central US and which are seen in several weather and climate models About 9 modelling centres so far have agreed to provide GCM-data 4-day hindcasts for MC3E-period as well as multi-month and multi-year AMIPsimulations Get in touch if you would like to participate: or Part II: Remaining cloud problems related to other parameterisations? Lack of deep convection related to missing interaction with Rockies and cold pools? Overestimation nocturnal boundary-layer clouds related to PBL-scheme or large-scale cloud scheme? Convection-permitting simulations for CONUS-domain have been started

69 Outlook Part I will be part of the CAUSES project Project with observationally-based focus, which evaluates the role of clouds, radiation and precipitation processes contributing to the surface temperature biases in the central US and which are seen in several weather and climate models About 9 modelling centres so far have agreed to provide GCM-data 4-day hindcasts for MC3E-period as well as multi-month and multi-year AMIPsimulations Get in touch if you would like to participate: or Part II: Remaining cloud problems related to other parameterisations? Lack of deep convection related to missing interaction with Rockies and cold pools? Overestimation nocturnal boundary-layer clouds related to PBL-scheme or large-scale cloud scheme? Convection-permitting simulations for CONUS-domain have been started

70 Thank you!

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